Multi-Criteria Simulation Optimization for COVID-19 Testing In Schools.
Winter Simulation Conference（2023）
Abstract
Evidence has shown that random screening tests are effective in reducing COVID-19 infections in schools. However, test administration may be hindered due to a limited budget or low participation caused by pandemic fatigue. Thus, we seek to balance the number of tests administered with end-of-semester infections. To do this we use an SEIR model to simulate SARS-CoV-2 transmissions within K-12 schools, design a multi-objective simulation optimization problem, and tune an NSGA-II algorithm to find the best testing schedules. We find the Pareto front of optimal schedules of screening tests, which can be used by stakeholders to inform test administration strategies. We discuss insights about the characteristics of optimal strategies, for example, when there are limited number of tests available or a desire to use few tests, the optimal plan is to perform the tests earlier in the semester and at higher intensity.
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Key words
Multi-objective Optimization,COVID-19 Testing,Simulation Optimization,School Test,Optimization Problem,Screening Test,Number Of Tests,Bootstrap Resampling,Optimal Schedule,Test Administration,Pareto Front,Multi-objective Optimization Problem,Testing Schedule,Random Screening,SEIR Model,Simulation Model,Rapid Diagnostic Tests,Middle School,Tuning Parameter,Test Day,Rapid Antigen Tests,Changes In Human Behavior,Frequent Testing,Week Of The Semester,Pareto Front Solutions,Beginning Of The Semester,Outside Of School,Manhattan Distance,PCR Test,Lot Of Tests
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